Revisiting Data Collection in Robotic Sensor Networks: A Budget-Constrained Traveling Salesman Perspective

We focus on robotic sensor networks (RSNs), wherein mobile data collectors or robots are dispatched into the sensor field to collect data from the sensor nodes, and study a new algorithmic problem called battery-constrained data collection in RSNs (BC-DCR). Given an RSN of sensor nodes with varying numbers of sensory data packets to be collected and a robot with limited battery power, the goal of the BC-DCR is to dispatch the robot into the sensor field to collect the maximum number of data packets before it runs out of battery power and returns to the depot for recharging. Although extensive research has been conducted to achieve various performance objectives of data collection in RSNs, not much work has focused on the robot's limited battery power. It is critical to consider the robot's limited battery power to optimize the data-collecting performance of a large-scale RSN. We show that at the core of the BC-DCR is a new variation of the classic traveling salesman problem called the Budget-Constrained Traveling Salesman Problem (BC-TSP), which has not been adequately solved. We design an Integer Linear Programming (ILP)-based optimal algorithm and a time-efficient iterative greedy algorithm to solve the BC-TSP. Via extensive simulations using real measurements of battery power and mobility models of robots, we show that a) our algorithms outperform the existing work by collecting 29.1% more packets with the same battery power of the robots and b) our BC-TSP-based approach achieves 32.02% more network lifetime of the RSN compared to the existing approach.